{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T21:25:13Z","timestamp":1730323513166,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":10,"publisher":"ACM","funder":[{"name":"National Key Research and Development Program of China grant","award":["2017YFB0902902"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,12,3]]},"DOI":"10.1145\/3452940.3453035","type":"proceedings-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T11:28:54Z","timestamp":1621250934000},"page":"493-498","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis Method of Power Consumption Characteristics of Residents in Low-Voltage Stations Based on Clustering Algorithm"],"prefix":"10.1145","author":[{"given":"Quan","family":"Xu","sequence":"first","affiliation":[{"name":"Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou, China"}]},{"given":"Xiangyu","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"given":"Fangyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"given":"Zhiyong","family":"Yuan","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"issue":"09","key":"e_1_3_2_1_1_1","first-page":"18","article-title":"Research on Electricity Users Classification Technology Based on Actual Load Curve","volume":"26","author":"Feng Xiaopu","year":"2010","unstructured":"Xiaopu Feng , Tiefeng Zhang , 2010 , \" Research on Electricity Users Classification Technology Based on Actual Load Curve \", Electric Power Science and Engineering , 26 ( 09 ): 18 -- 22 . Xiaopu Feng, Tiefeng Zhang, 2010, \"Research on Electricity Users Classification Technology Based on Actual Load Curve\", Electric Power Science and Engineering, 26(09):18--22.","journal-title":"Electric Power Science and Engineering"},{"issue":"08","key":"e_1_3_2_1_2_1","first-page":"1742","article-title":"Transformer Winding Temperature Soft Measurement Model Based on Particle Swarm Optimization-Support Vector Regression","volume":"33","author":"Peng Daogang","year":"2018","unstructured":"Daogang Peng , Yuewei Chen , 2018 , \" Transformer Winding Temperature Soft Measurement Model Based on Particle Swarm Optimization-Support Vector Regression \", Transactions of China Electrotechnical Society , 33 ( 08 ): 1742 -- 1749 +1761. Daogang Peng, Yuewei Chen, 2018, \"Transformer Winding Temperature Soft Measurement Model Based on Particle Swarm Optimization-Support Vector Regression\", Transactions of China Electrotechnical Society, 33(08):1742--1749+1761.","journal-title":"Transactions of China Electrotechnical Society"},{"issue":"02","key":"e_1_3_2_1_3_1","first-page":"70","article-title":"Windspeed prediction method based on SVR and multi-parameter optimization of GA","volume":"21","author":"Zhu Xiaoxun","year":"2017","unstructured":"Xiaoxun Zhu , Bochao Xu , 2017 , \" Windspeed prediction method based on SVR and multi-parameter optimization of GA \", Electric Machines and Control , 21 ( 02 ): 70 -- 75 . Xiaoxun Zhu, Bochao Xu, 2017, \"Windspeed prediction method based on SVR and multi-parameter optimization of GA\", Electric Machines and Control, 21(02):70--75.","journal-title":"Electric Machines and Control"},{"issue":"06","key":"e_1_3_2_1_4_1","first-page":"198","article-title":"Parameters Optimization of Support Vector Regression Based on Differential Evolution","volume":"28","author":"Chen Tao","year":"2011","unstructured":"Tao Chen , 2011 , \" Parameters Optimization of Support Vector Regression Based on Differential Evolution \", Computer Simulation , 28 ( 06 ): 198 -- 201 . Tao Chen, 2011, \"Parameters Optimization of Support Vector Regression Based on Differential Evolution\", Computer Simulation, 28(06):198--201.","journal-title":"Computer Simulation"},{"issue":"06","key":"e_1_3_2_1_5_1","first-page":"33","article-title":"Parameter Selection of Support Vector Regression Based on Cuckoo Search Algorithm","volume":"46","author":"He Silu","year":"2014","unstructured":"Silu He , Jianhua Han , 2014 , \" Parameter Selection of Support Vector Regression Based on Cuckoo Search Algorithm \", Journal of South China Normal University (Natural Science Edition) , 46 ( 06 ): 33 -- 39 . Silu He, Jianhua Han, 2014, \"Parameter Selection of Support Vector Regression Based on Cuckoo Search Algorithm\", Journal of South China Normal University (Natural Science Edition), 46(06):33--39.","journal-title":"Journal of South China Normal University (Natural Science Edition)"},{"key":"e_1_3_2_1_6_1","first-page":"12","article-title":"Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM","volume":"1","author":"Xiaoqun Liao","year":"2019","unstructured":"Liao Xiaoqun , Kang Xiaofan , 2019 , \" Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM \" The 4th International Conference on Intelligent Transportation, Big Data & Smart city, China Changsha , 1 . 12 - 11 .13:305--308. Liao Xiaoqun, Kang Xiaofan, 2019, \"Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM\" The 4th International Conference on Intelligent Transportation, Big Data & Smart city, China Changsha, 1.12-1.13:305--308.","journal-title":"The 4th International Conference on Intelligent Transportation, Big Data & Smart city, China Changsha"},{"issue":"01","key":"e_1_3_2_1_7_1","first-page":"68","article-title":"Clustering Analysis of User Power Interaction Behavior Based on Self-organizing Center K-means Algorithm","volume":"40","author":"Bingyu Zhou","year":"2019","unstructured":"Zhou Bingyu , Liu Bo , 2019 , \" Clustering Analysis of User Power Interaction Behavior Based on Self-organizing Center K-means Algorithm \", Electric Power Construction , 40 ( 01 ): 68 -- 76 . Zhou Bingyu, Liu Bo, 2019, \"Clustering Analysis of User Power Interaction Behavior Based on Self-organizing Center K-means Algorithm\", Electric Power Construction, 40(01):68--76.","journal-title":"Electric Power Construction"},{"issue":"05","key":"e_1_3_2_1_8_1","first-page":"46","article-title":"Short-term Load Forecasting Method Based on Empirical Mode Decomposition and Feature Correlation Analysis","volume":"43","author":"Kong Xiangyu","year":"2019","unstructured":"Xiangyu Kong , Chuang Li , 2019 , \" Short-term Load Forecasting Method Based on Empirical Mode Decomposition and Feature Correlation Analysis \", Automation of Electric Power Systems , 43 ( 05 ): 46 -- 56 . Xiangyu Kong, Chuang Li, 2019, \"Short-term Load Forecasting Method Based on Empirical Mode Decomposition and Feature Correlation Analysis\", Automation of Electric Power Systems, 43(05):46--56.","journal-title":"Automation of Electric Power Systems"},{"issue":"05","key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3788\/LOP57.053002","article-title":"Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization","volume":"57","author":"Yang Youliang","year":"2020","unstructured":"Youliang Yang , Lu Wang , 2020 , \" Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization \", Laser & Optoelectronics Progress , 57 ( 05 ): 264 -- 271 . Youliang Yang, Lu Wang, 2020, \"Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization\", Laser & Optoelectronics Progress, 57(05): 264--271.","journal-title":"Laser & Optoelectronics Progress"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"W. Zhang T. Li 2010 \"Dynamic Parameter Selection Based on Trigonometric Function in Particle Swarm Optimization\" 2010 Third International Joint Conference on Computational Science and Optimization Huangshan 283--286. W. Zhang T. Li 2010 \"Dynamic Parameter Selection Based on Trigonometric Function in Particle Swarm Optimization\" 2010 Third International Joint Conference on Computational Science and Optimization Huangshan 283--286.","DOI":"10.1109\/CSO.2010.109"}],"event":{"name":"ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering","acronym":"ICITEE2020","location":"Changde City Hunan China"},"container-title":["Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3452940.3453035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T20:21:31Z","timestamp":1681503691000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3452940.3453035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,3]]},"references-count":10,"alternative-id":["10.1145\/3452940.3453035","10.1145\/3452940"],"URL":"https:\/\/doi.org\/10.1145\/3452940.3453035","relation":{},"subject":[],"published":{"date-parts":[[2020,12,3]]},"assertion":[{"value":"2021-05-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}